Neural networks have been successfully applied. However, lots of parameters and large-scale computation of a neural network become a huge challenge to neural network application. On one hand, lots of parameters make a very high requirement on a storage capacity and also result in high memory access energy consumption. On the other hand, large-scale computation makes a very high requirement on design of a computation unit and also results in high computation energy consumption. Therefore, how to reduce parameters and computation amount of a neural network becomes a problem urgent to be solved.